Methods and apparatus for providing a ML decoding pipeline for QPSK MIMO OFDM receivers

ABSTRACT

Methods and apparatus are provided for computing reliability values in a pipeline. A reliability value portion is computed, with control circuitry in a first pipeline stage, based on a difference between a received input signal value and an expected value. The reliability value portion is combined, in a second pipeline stage that follows the first pipeline stage, with a first value derived from the received input signal to generate a first reliability value. A determination is made as to whether to update the first reliability value in a third pipeline stage based on a combination of the first reliability value with a second reliability value that corresponds to a second value derived from the received input signal.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. patent application Ser. No.12/140,022, filed Jun. 16, 2008, now U.S. Pat. No. 8,098,774 B1; andU.S. Provisional Application Nos. 60/944,245, filed Jun. 15, 2007 and60/975,639, filed Sep. 27, 2007, each of which is hereby incorporated byreference herein in its entirety.

BACKGROUND OF THE INVENTION

The present invention is directed to methods and apparatus for providinga Maximum Likelihood (ML) decoding pipeline for Quadrature Phase-ShiftKeying (QPSK) Multiple-Input-Multiple-Output (MIMO) OrthogonalFrequency−Division Multiplexing (OFDM) receivers, and more particularlyto pipelining Log-Likelihood Ratio (LLR) computations associated withmultiple stream input signals to increase data throughput.

Typically, in QPSK MIMO OFDM receivers, certainty of the received signalvalues (i.e., LLR) can be computed using the communications modely_(k)=H_(k)*x_(k)+n, where y_(k) is the received vector at a tone k,x_(k) is the transmitted vector at the tone k and H_(k) is the channelresponse. In particular, the LLR values can be determined from a softmetric (SM) defined for each bit position (SM_(b), where b is the bitposition) as the absolute value of the difference between the value atthe bit position of the received signal y and the value at the bitposition of the possibly transmitted signal x multiplied by the channelresponse H (i.e., SM_(b)=|Y_(b)−x_(b)*H_(b)|). The LLR for a particularbit position can then be determined by computing the difference betweenminimum SM values (minSM) corresponding to each of the possiblytransmitted values of that bit position (e.g., (minSM₀ for bit position0 having a value of 0) minus (minSM₀ for bit position 0 having a valueof 1)). The LLR values thus indicate a confidence level in the receivedbit value at the particular bit position of a received vector y.

LLR computations generally consume a large amount of processing powerand take a long period of time for multiple stream input signals. Thisis because computing a LLR for a particular bit position b of a receivedsignal requires the system to find, for every stream, the minimum valueof the difference between the received signal value at the bit positionand every possible permutation of values which that bit position couldhave been (i.e., the minimum SMO. That is, the system has to guess whatthe signal x was by trying every combination in comparison to theactually received signal y. The lowest metric value indicates thegreatest likelihood that the value of the bit position is the value ofthe particular permutation of the x vector which led to that lowestmetric value (i.e., the smallest difference between the guessed value xand the received value y). This determination must be made for every bitposition of every stream of the received signal and is computationallyintensive.

Additionally, in a QPSK 3×Nr receiver, every signal stream of a threestream vector contains at least two bit positions which can take on oneof four values {(0,0), (0,1), (1,0), (1,1)} which correspond to thecomplex vector values {1+j, 1−j, −1+j, −1−j}. Thus, in such a receiver,64 metrics or SM_(b) values need to be computed for each tone of areceived signal to find the LLR for every bit position of the threestreams.

Therefore, because the system has to compute every permutation of atransmitted signal in comparison to the received signal streams, thecomplexity of finding LLR values increases exponentially with the numberof streams used to transmit a signal.

Accordingly, more efficient computations of LLR values become criticalas the number of streams increases and faster computation of thosevalues are necessary for high speed receivers.

SUMMARY OF THE INVENTION

In accordance with the principles of the present invention, methods andapparatus provide a decoding pipeline for QPSK MIMO OFDM receivers. Inparticular, the LLR value computations are pipelined to increase thenumber of LLR values that are computed per clock cycle.

In one embodiment, portions of the metric SM that are associated witheach of the received signal streams are computed in parallel in a firstpart of the pipeline. For example, the received signal may be a threestream signal y. The portions that are computed correspond to thesubtraction of the received signal y and every permutation of two of thethree streams x₁, x₂, and x₃. For example, the portions of SM may becomputed in accordance with y−h₂*x₂−h₃*x₃ for every stream of thereceived signal y in parallel, where h_(i) is a subset corresponding toa particular stream i of the channel response H.

In a second part of the pipeline, the computed metric portions aresubtracted from a different permutation of h₁*x₂ every clock cycle tocompute the metric. Thus, after four clock cycles, for a QPSK signalhaving four possible values, the computation of every permutation ofu=y−h₁*x₁−h₂*x₂−h₃*x₃ is completed. After each one of the clock cycleswhere a metric u is produced for one permutation of x₁ (e.g., aftery−h₁*x₁−h₂*x₂−h₃*x₃ has been computed for one permutation of x₁), theabsolute value of the metric is taken and the metrics that correspond toa particular signal stream are accumulated to produce metric M valueswhich are later used to compute the soft metric and LLR values. Inparticular, for each permutation of x₁ there may be 16 M valuesproduced. Thus, after four clock cycles 64 M values may be produced thatcorrespond to every permutation of x₁, x₂, and x₃ in u.

In a third part of the pipeline, a minimum value of the M values thataffect a particular state of a received stream (i.e., a value of one ofthe bit positions of one of the received streams) is determined. Thestate associated with that bit position is then set to that minimum Mvalue. At each clock cycle a new minimum value is computed and comparedwith a previously stored minimum value for that state. This is becausethe minimum values are computed beginning with the first 16 M valuesthat are associated with one permutation of x₁. Accordingly, it isnecessary to compare the minimum M values from one permutation of x₁with the minimum M values that result from another permutation of x₁.

In the final state of the pipeline, the LLR value is computed for eachbit position of the received signal y. The LLR is computed by taking thedifference between each of the minimum M values that correspond to aparticular bit position of a stream of the received signal y.

In another embodiment, portions of the metric SM that are associatedwith each one of the received signal streams are computed one stream perclock cycle in a first part of the pipeline. In particular, the firstpart of the pipeline computes y−h₀*x₀−h₁*x₁ for every permutation of x₀and x₁ one signal stream per clock cycle. The first part also computesevery permutation of h₂*x₂ to be combined with y−h₀*x₀−h₁*x₁ insubsequent parts of the pipeline.

In the next part of the pipeline, the M values are computed for eachpermutation of x₂ by subtracting the parts computed in the first part ofthe pipeline and computing the absolute value. In particular, 16 Mvalues are computed for each permutation of x₂ associated with onestream of the received signal y in each clock cycle. Thus, after threeclock cycles for a three stream input signal, all of the M values foreach of the received signal streams are computed.

The minimum of the computed M values is determined simultaneously witheach of the M values associated with a particular stream of the receivedsignal at the next part of the pipeline. The state values are set to bethe minimum of the computed M values that affect a particular state. Theminimum M values that affect a particular state and that are associatedwith different permutations of x₂ are compared to find the minimum amongthose M values and the state values are set to be that minimum value.

At the final part of the pipeline, the state values that correspond to aparticular bit position of each stream of the received signal arecombined to compute the LLR value for that bit position. In particular,the LLR value is computed by taking the difference between two statevalues that correspond to each bit position of a received signal stream.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other features of the present invention, its nature andvarious advantages will be more apparent upon consideration of thefollowing detailed description, taken in conjunction with theaccompanying drawings in which:

FIG. 1 is a diagram of an illustrative ML decoding pipeline receiversystem in accordance with an embodiment of the present invention;

FIG. 2 is an illustrative LLR computations model in accordance with anembodiment of the present invention;

FIG. 3 is a detailed illustration of an ML decoding pipeline inaccordance with an embodiment of the present invention;

FIG. 4 is an illustrative diagram of metric state computation trees inaccordance with an embodiment of the present invention;

FIG. 5 is a detailed illustration of metric state portion computationcircuitry in accordance with an embodiment of the present invention;

FIG. 6 is a detailed illustration of metric state computation circuitryin accordance with an embodiment of the present invention;

FIG. 7 is a detailed illustration of metric state update circuitry inaccordance with an embodiment of the present invention;

FIGS. 8-10 are detailed illustrations of metric state update comparatortrees in accordance with an embodiment of the present invention;

FIG. 11 is a detailed illustration of LLR computation pipeline stages inaccordance with an embodiment of the present invention;

FIG. 12 is a detailed illustration of an ML decoding pipeline inaccordance with another embodiment of the present invention;

FIG. 13 illustrates a process performed by ML decoding pipeline inaccordance with an embodiment of the present invention;

FIG. 14A is a block diagram of an exemplary hard disk drive that canemploy the disclosed technology;

FIG. 14B is a block diagram of an exemplary digital versatile disc thatcan employ the disclosed technology;

FIG. 14C is a block diagram of an exemplary high definition televisionthat can employ the disclosed technology;

FIG. 14D is a block diagram of an exemplary vehicle that can employ thedisclosed technology;

FIG. 14E is a block diagram of an exemplary cell phone that can employthe disclosed technology;

FIG. 14F is a block diagram of an exemplary set top box that can employthe disclosed technology; and

FIG. 14G is a block diagram of an exemplary media player that can employthe disclosed technology.

DETAILED DESCRIPTION

This invention generally relates to decoding a multi-stream signal thatis received in parallel and obtaining LLR values for bit positions inthe signal in pipeline stages. For illustrative purposes, this inventionwill be described in the realm of a 3×Nr MIMO system and in particulardecoding a complex QPSK signal that is received in three streams.

FIG. 1 is a diagram of an illustrative ML decoding pipeline receiversystem 100 in accordance with an embodiment of the present invention.System 100 includes a receiver 110, an ML decoder pipeline 120,processing circuitry 130 and a channel memory 150. Receiver 110 iscoupled through a communications medium 142 to an antenna 140 or someother signal receiving device (e.g., a cable, infrared, etc.).

Receiver 110 may perform some computations to determine or estimate thechannel response H of the transmission medium of antenna 140. Receiver110 may store the channel response H to channel memory 150 in order tobe used in performing LLR computations for verifying the received signalvalues. Receiver 110 also receives the signal y through communicationsmedium 142. Receiver 110 may store the received signal y to channelmemory 150 to allow ML decoder 120 to perform LLR computations on thesignal y.

Signal y may be received as any number of parallel streams y_(i) where irepresents the stream of the received signal. For illustrative purposes,the present invention will be described in the context of a complexsignal y that is received as three parallel streams y₁, y₂, y₃.

ML decoder pipeline 120 may receive the channel response H and receivedsignal y directly from receiver 110. Alternatively, ML decoder pipeline120 may retrieve the channel response H and received signal y fromchannel memory 150. ML decoder pipeline 120 performs pipelinedcomputations using the channel response H and the signal y to determineLLR values for each bit position of the signal y. In particular, MLdecoder pipeline 120 can compute 64 metrics for each tone of thereceived signal in a pipeline fashion. More specifically, ML decoderpipeline 120 computes some of the metrics based on one permutation ofone of the signal streams (e.g., x₁) and every permutation of the othersignal streams (e.g., x₂, x₃) in some pipeline stages while at the sametime in other pipeline stages computing some of the metrics based on adifferent permutation of stream x₁. For example, this may done until allpermutations of the three streams have been used to compute the 64metrics. The metrics are then compared in parts to find the minimummetric values in different pipeline stages and LLR values are thencomputed in later pipeline stages. Pipelining the metric computationsincreases the speed and efficiency of the LLR computations sincemultiple metric computations can be performed simultaneously (i.e., indifferent pipeline stages). This also allows the system to compute LLRvalues for one tone of the received signal at the same time the systemcomputes LLR values for another tone of the received signal.

One implementation of ML decoder pipeline 120 will be provided in moredetail in connection with FIGS. 4-11, and another implementation of MLdecoder pipeline 120 will be provided in connection with FIG. 12.

ML decoder pipeline 120 uses the computed LLR values to verify ordetermine the maximum likelihood that a value in a particular bitposition of the received signal is correct. ML decoder pipeline 120outputs the ML values or LLR values of the received signal viacommunications link 122 to processing circuitry 130 for furthercomputations. Processing circuitry 130 may perform computations on thereceived signal values such as forward error correction decoding, datamanipulations, etc.

FIG. 2 is an illustrative LLR computations model 200 in accordance withan embodiment of the present invention. LLR computations model 200includes three streams. Each stream has first and second bit positionsthat can each take on one of two values. Thus, as shown, stream 1 hasfour metric states (S1-S4) where S1 and S2 are the metric states thatcorrespond to the values which the first bit of stream 1 can be (i.e.,‘0’ or ‘1’) and S3 and S4 are the metric states that correspond to thevalues which the second bit of stream 1 can be (i.e., ‘0’ or ‘1’). Inparticular, identifier 210 indicates the bit position of the stream thatis associated with the metric state and the value with which that bitposition is associated. In order to compute all of the LLR values 220for each stream, it is necessary to compute 12 metric state valuesS1-S12. Each metric state value is computed by finding the minimum SMvalues that are associated with a particular bit position value.

To compute the LLR 220 for the first bit of stream 1, for example, theS1 metric state value is subtracted from the S2 metric state value.Accordingly, a more negative LLR value will indicate that the value ofthe first bit position of stream 1 is more likely to be ‘0’ whereas amore positive LLR value will indicate that the value of the first bitposition of stream 1 is more likely to be ‘1’. Since 12 metric statevalues are computed, six LLR values 220 are produced each indicating thelevel of confidence that a particular bit position of one of the streamsis a certain value. Identifier 222 of LLR value 220 indicates the streamassociated with the LLR value and the identifier 224 indicates the bitposition to which the LLR value corresponds.

FIG. 3 is a detailed illustration of an ML decoding pipeline 120 inaccordance with an embodiment of the present invention. ML decodingpipeline 120 includes metric state portion computation circuitry 310,metric logic circuitry 320, state update circuitry 330 and LLRcomputation circuitry 340.

Metric state portion computation circuitry 310 receives the channelresponse and the received signal streams and computes a portion of themetric SM. For example, in one embodiment, metric state portioncomputation circuitry 310 computes the portion of SM in accordance withu=y−(h₁x₁+h₂x₂+h₃x₃) for every permutation of x₂ and x₃ and a firstpermutation of x₁ in a first four clock cycles for the three y streamsin parallel. During those first four clock cycles, metric state portioncomputation circuitry 310 also simultaneously computes u for everypermutation of x₂ and x₃ and a second permutation of x₁—hence thepipeline. Because an input stream can take on one of four values, thereneed to be four permutation computations of x₁ in u. This embodiment ofmetric state portion computation circuitry 310 will be discussed in moredetail in connection with FIGS. 4 and 5.

In another embodiment, the portions of u that metric state portioncircuitry 310 computes are h₁*x₁ h₂*x₂ and h₃*x₃ and (y−h₁*x₁−h₂*x₂) forevery permutation of x₁, x₂ and x₃ for one of the received streams infour clock cycles and computes the metric state portions of differentstreams of y during the four clock cycles. This embodiment of metricstate portion computation circuitry 310 will be discussed in more detailin connection with FIG. 12.

Metric state portion circuitry 310 provides the metric state portions tometric logic circuitry 320 via communications link 312. Metric logiccircuitry 320, in one embodiment, computes the metric M values for eachpermutation of x₁ from the received u values in accordance withM=|u_(i)| where i is the permutation of x₃. In some implementations, Mmay be computed as an approximation in accordance with|u_(i)|=max(|u_(iI)|,|u_(iQ)|)+(5/16)*min(|u_(iI)|,|u_(iQ)|) where i isthe permutation of x₃, I is the real component of the u values and Q isthe imaginary component of the u values. Thus, in four clock cycles allof the M values of the three streams can be computed where in each clockcycle M₁₋₁₆ are computed which are 16 of the 64 necessary metric statevalues. In another embodiment, metric logic circuitry 320 computes the|u_(i)| from (y−h₁*x₁−h₂*x₂)−h₃x₃ for each permutation of x₃ once everyclock cycle. Metric logic circuitry 320 will be discussed in more detailin connection with FIGS. 6 and 12.

Metric logic circuitry 320 provides the computed metric state values Mto state update circuitry 330 via communications link 322. State updatecircuitry 330 compares M values that correspond to particular bitpositions of the various signal streams to determine which of the Mvalues associated with that bit position is the minimum value. Stateupdate circuitry 330 then sets the state value (i.e., S1, S2, . . . , orS12) to be the minimum computed value among the M values. For example,to find the minimum M value for state S10, state update circuitry 330finds the minimum value among M₃, M₄, M₇, M₈, M₁₀, M₁₁, M₁₂, M₁₅, and M₁for every permutation of x₁ because, for example, as shown in FIG. 4,those M values affect the bit position corresponding to S10 (i.e., wherethe first bit of x₃ is equal to ‘1’). Because it takes four clock cyclesto compute all of the M values for each of the four permutations of x₁,the state update circuitry outputs the 12 state values in four clockcycles.

State update circuitry 330 provides the 12 computed state values to LLRcomputation circuitry 340 via communications link 332. LLR computationcircuitry computes the LLR for each bit position based on the statevalues associated with that bit position. For example, LLR computationcircuitry computes the LLR₂₁ (FIG. 2) (i.e., the second bit position ofthe second stream) by computing the difference between state values S7and S8. LLR computation circuitry 340 provides the computed LLR valuesto processing circuitry 130 via communications link 122.

FIG. 4 is an illustrative diagram of metric state computation trees 400in accordance with an embodiment of the present invention. Metric statecomputation trees 400 show all of the computations that are necessary tocompute LLR values for each bit position of each stream of a receivedsignal. Each sub-tree of metric state computation trees 400 correspondsto one permutation of x₁ and accordingly, since x₁ can be any one offour values, four different computation sub-trees are shown in trees400. It is clear from seeing the various sub-trees, that each sub-treecan be computed independent of another sub-tree. Thus, the sub-trees, inone embodiment, may be computed in a pipeline which allows all of the Mvalues associated with each sub-tree to be computed simultaneously indifferent pipeline clock cycles or stages and produce 64 M values infour clock cycles.

At each node or leaf of trees 400, for simplicity in referring to aparticular permutation computation below, a label (i.e., a letter of thealphabet) represents one permutation of x₁, x₂, or x₃. For example, thelabel ‘E’ represents the permutation of x₁ being “00”, the label “−E”represents the permutation of x₁ being “11”, the label ‘F’ representsthe permutation of x₁ being “01”, and the label “−F” represents thepermutation of x₁ being “10”. Similarly, the label ‘C’ represents thepermutation of x₂ being “00”, the label “−C” represents the permutationof x₂ being “11”, the label ‘D’ represents the permutation of x₂ being“01”, and the label “−D” represents the permutation of x₂ being “10”.Finally, the label ‘A’ represents the permutation of x₃ being “00”, thelabel “−A” represents the permutation of x₃ being “11”, the label ‘B’represents the permutation of x₃ being “01”, and the label “−B”represents the permutation of x₃ being “10”. Accordingly, thecomputation of E+(−D)+(B) corresponds to the computation ofh₁*x₁+h₂*x₂+h₃*x₃ for the permutations of x₁=“00”, x₂=“10” and x₃=“01”.

The only dependencies that exist in the LLR computations are withrespect to the minimum M value determinations. This is because eachsub-tree produces 16 different M values corresponding to a differentpermutation of x₁ and those M values that affect a particular state(FIG. 2) need to be compared to one another. In particular, one sub-treemay produce M values which affect a particular state that is alsoaffected by an M value produced by one of the other sub-trees.Accordingly, the minimum M value computations (i.e., state updatecomputations) require four clock cycles (one clock cycle for each set ofM values of a particular sub-tree) in order to compare all of the Mvalues from each sub-tree that affect a particular state.

For example, the first tree corresponds to the permutation of x₁ beingequal to “00” labeled as E. At the lowest level of the first tree are 16leaves that each correspond to a different M value of that x₁permutation. In particular, the computation associated with thepermutation of x₁ being “00”, x₂ being “01” (labeled as D) and x₃ being“00” (labeled as A) results in the M₅ value. Accordingly, it can be seenthat each tree provides a different set of 16 M values that eachcorrespond to a different permutation of x₁ (that is combined with allpermutations of x₂ and x₃) and thereby 64 M values are provided in totalwhich are necessary for computing the LLR values for each bit positionof the three received y streams.

With reference to computation trees 400, metric state portioncomputation circuitry 310, computes one of the four trees. Inparticular, metric state portion computation circuitry 310 can computeh₁x₁+h₂x₂+h₃x₃ for every permutation of x₂ and x₃ and one permutation ofx₁ (i.e., E+(C−A), E+(C+B), E+(C+(−B)), E+(C+(−A)), E+(D+A), . . . ,E+(−C+(−A)). This produces the 16 M values that correspond to the Epermutation of x₁.

Computation trees 400 also provide information as to which states aparticular M value affects. State update circuitry 330 uses thisinformation to determine which M values need to be compared with eachother to find a minimum M value for a particular state. In particular,when computing the minimum M values that correspond to a particularstate, it is necessary to exclude M values which do not have an effecton that state. Thus, as shown in the first computation tree 400 (i.e.,the tree associated with the value E), M₁ affects states S9 and S11because A (which is a leaf associated with M₁) corresponds the value“00” of the stream 3 and as shown in FIG. 2, S9 corresponds to stream 3,bit position 0 being equal to ‘0’ and S11 corresponds to stream 3, bitposition 1 being equal to ‘0’. Similarly, M₁ also affects states S5 andS7 because C (which is a node associated with M₁) corresponds the value“00” of the stream 2 and as shown in FIG. 2, S5 corresponds to stream 2,bit position 0 being equal to ‘0’ and S7 corresponds to stream 3, bitposition 1 being equal to ‘0’. Finally, it can be determined that M₁also affects states S1 and S3 since those states are associated with Ewhich corresponds to the value “00” of stream 1. Thus, M₁ affects statesS1, S3, S5, S7, S9 and S11.

One can similarly determine which other M values of all the othersub-trees affect the states which M₁ corresponding to the E sub-treeaffects (i.e., states S1, S3, S5, S7, S9 and S11). For example, amongother M values, S5 is affected by M₁₋₈ and S7 is affected by M₁₋₄ andM₉₋₁₂. Thus, when computing the minimum M value for state S5, M₁₋₈ haveto be compared to one another to find the minimum among them and whencomputing the minimum M value for state S7, M₁₋₄ and M₉₋₁₂ have to becompared to one another to find the minimum among them.

FIG. 5 shows one implementation of metric state portion computationcircuitry 310 (FIG. 3). In particular, metric state portion circuitry500 (FIG. 5) includes several complex adders 510, 520, 530 and 540.Metric state portion circuitry 500 computes in four stages (i.e., fourpipeline clock cycles) one sub-tree of trees 400. For example, metricstate portion circuitry 500 can compute the sub-tree corresponding tothe one permutation of x₁ being equal to “00” and all permutations of x₂and x₃ in four clock cycles. Metric state portion circuitry 500 beginscomputing different sub-trees at each stage of the four stages to allowsimultaneous computation of the various sub-trees. It should beunderstood, that once all four sub-trees corresponding to one tone ofthe received signal have been computed, metric state portion computationcircuitry 500 may begin computing the four sub-trees corresponding toanother tone of a received signal. Thus, multiple tones of differentsignals may be computed in the decoder pipeline 120 simultaneously.

FIG. 11 is a detailed illustration of LLR computation pipeline stages1100 in accordance with an embodiment of the present invention. Inparticular, FIG. 11 shows the pipelined computations associated with thesub-tree of trees 400 (FIG. 4) corresponding to one permutation of x₁being equal to “00”. Metric state portion circuitry 500 computes thevalues shown in computation pipeline stages 1100 associated with Times1-4.

Referring back to FIG. 5, stage 1 of metric state portion circuitry 500computes every permutation of stream x_(i) multiplied by the channelresponse associated with stream i. For example, in stage 1 (i.e., afirst clock cycle), complex adder 510 retrieves from channel memory 150(or receives from receiver 110 (FIG. 1) the channel response h₃(associated with stream 1) and computes all permutations of h₃ and x₃ tooutput h₃*x₃. In particular, x_(i) corresponds to all permutations ofone of the received streams y_(i) and thus can be equal to one of fourvalues (i.e., (0,0), (1,0), (0,1), (1,1)). Accordingly, the output ofcomplex adder 510 is h₃ multiplied by each of those four possibilitiesor permutations. These computations correspond to the values at theleaves of each sub-tree of trees 400 (FIG. 4) (i.e., the A, −A, B, and−B values).

It should be understood that x₃ need not be received by complex adder510 since every possibility of x₃ can be generated by complex adder 510and multiplied by h₃. However, in some embodiments, channel memory 150may store every permutation of x₁, x₂, and x₃ and thus provide thenecessary values to complex adders 510, 520, 530 or 540. In some otherembodiments, receiver 110 (FIG. 1) may generate every permutation of x₁,x₂, and x₃ and thus provide the necessary values directly to metricstate portion circuitry 500.

Stage 2 of metric state portion circuitry 500 computes using complexadder 520 every permutation of stream x₁₊₁ multiplied by the channelresponse associated with stream i+1. These computations may be performedin a similar fashion as those associated with the h₃*x₃ computation.These computations correspond to the values at the second level nodes ofeach sub-tree shown in trees 400 (FIG. 4) (i.e., the C, −C, D, and −Dvalues).

Complex adder 520 also retrieves from channel memory 150 (FIG. 1) orreceives from receiver 110 the values corresponding to the three streamsof the received signal y_(z) where z represents one of the threereceived signal streams for which the metric is being computed. Inparticular, complex adder 520 computes y_(z)−h₃*x₃ (i.e., y₁−h₃*x₃,y₂−h₃*x₃ and y₃−h₃*x₃) in parallel at the same time (i.e., in the samestage) as it computes h₂*x₂ (h_(i+1)*x_(i+1)).

Stage 3 of metric state portion circuitry 500 computes using complexadder 530 one permutation of stream x₁₊₂ multiplied by the channelresponse associated with stream i+2. This computation correspond to thevalues at the first level of one sub-tree shown in trees 400 (FIG. 4)(i.e., the E, −E, F, or −F values). Every pipeline clock cycle aftercomputing the values at the first level of one sub-tree, complex adder520 computes the values at the first level of one of the othersub-trees. Thus, after four clock cycles, all the values of each thesub-tree are computed and can be used to compute the M values.

Complex adder 520 also computes the portion of metric corresponding toy−h₃*x₃−h₂*x₂ by subtracting the values it receives from complex adder520. In particular, complex adder 520 computes y₁−h₃*x₃−h₂*x₂,y₂−h₃*x₃−h₂*x₂ and y₃−h₃*x₃−h₂*x₂ in parallel at the same time (i.e., inthe same stage) as it computes the one permutation of (h₁*x₁), where thei represents the permutation of x₁.

Stage 4 of metric state portion circuitry 500 computes using complexadder 540 the u_(z) values by subtracting the values it receives fromcomplex adder 530 where z represents one of the three received signalstreams for which the metric is being computed. In particular, complexadder 540 computes u₁=y₁−h₃*x₃−h₂*x₂−(h₁*x₁)_(i),u₂=y₂−h₃*x₃−h₂*x₂−(h₁*x₁)_(i) and u₃=y₃−h₃*x₃−h₂*x₂−(h₁*x₁)_(i).

The next step necessary to compute the metric is to compute the absolutevalues of u_(z). Then, each of the absolute values of u_(z) associatedwith the particular permutation of x₁ have to be summed to obtain the Mvalues for the particular sub-tree. Metric logic circuitry 320 (FIG. 3)performs these computations.

FIG. 6 shows one implementation of metric logic circuitry 320 (FIG. 3).In particular, metric logic circuitry 600 includes absolute valuecircuitry 610 and a complex adder 620. Metric logic circuitry 600implements LLR computation pipeline stages 1100 (FIG. 11) associatedwith times 5 and 6.

For example, in a one stage, which is stage 5 of ML decoder pipeline 120(FIG. 1), absolute value circuitry 610 receives the u_(z) values 312from metric state portion computation circuitry 310 and computes theabsolute value of u_(z). In another stage, which is stage 6 of MLdecoder pipeline 120, complex adder 620 sums all of the |u_(z)| toproduce 16 M values (i.e., M₁₋₁₆). In particular, each M value that isproduced corresponds to a different combination of E+(C, −C, D, or−D)+(A, −A, B, or −B).

At each clock cycle, 16 different M values are produced where each setcorresponds to a different permutation of x₁ or a different sub-tree oftrees 400 (FIG. 4). Thus, at the end of four clock cycles, metric logiccircuitry 600 produces M values corresponding to different combinationsof E+(C, −C, D, or −D)+(A, −A, B, or −B), −E+(C, −C, D, or −D)+(A, −A,B, or −B), F+(C, −C, D, or −D)+(A, −A, B, or −B), and −F+(C, −C, D, or−D)+(A, −A, B, or −B).

After the M values are computed for one of the trees 400, the M valuesthat affect a particular state are compared by state update circuitry330 to find the minimum M value and store that value as the value forthe affected state.

FIG. 7 shows one implementation of state update circuitry 330 (FIG. 3).In particular, state update circuitry 700 includes a first comparatortree 710, a second comparator tree 720, a third comparator tree 730, andstate storage devices 740, 750 and 760. State update circuitry 700implements LLR computation pipeline stages 1100 (FIG. 11) associatedwith times 7-10.

Each comparator tree 710, 720 and 730 receives the M values 322 computedby metric logic circuitry 320 (FIG. 3). First comparator tree 710includes comparison circuitry that compares the M values that affectstates S9 and S10. As discussed above in connection with trees 400 (FIG.4), for example, state S9 is affected by M₁, M₂, M₅, M₅, M₉, M₁₀, M₁₃,and M₁₄. Accordingly, first comparator tree 710 includes circuitry thatfinds the minimum among M₁, M₂, M₅, M₅, M₉, M₁₀, M₁₃, and M₁₄. Firstcomparator tree 710 compares that minimum value that affects state S9and with a previously stored minimum value for state S9 (received fromstorage device 740) and finds the minimum among those values. Inparticular, because each sub-tree of trees 400 includes M values thatmay affect some of the states S1-S12, each comparator tree 710, 720 and730 has to compare a previously stored minimum value for the state withnewly computed values (i.e., the M values associated with a particularsub-tree received at every clock cycle) associated with that state.First comparator tree 710 then stores that minimum value in a locationon storage device 740 that corresponds to S9.

FIG. 8 is an exemplary implementation of first comparator tree 710. Inparticular, as shown in FIG. 8, first comparator tree 710 may includemultiple comparators arranged in a tournament tree fashion to find aparticular minimum M value for states S9 or S10 among a number of Mvalues that affect those respective states.

Second and third comparator trees 720 and 730 operate in a similarmanner as first comparator tree 710 to produce minimum state values. Inparticular, second comparator tree 720 receives 16 M values 322 andcompares the M values that affect states S11 and S12 to find the minimumamong them. Also, second comparator tree 720 receives a previouslystored minimum value for S11 and S12 from storage device 750 andcompares those values to the newly computed minimum values to find theminimum among them. Second comparator tree 720 outputs and stores theminimum state S11 and S12 values as the new minimum values for thosestates in storage device 750.

FIG. 9 is an exemplary implementation of second comparator tree 720. Inparticular, as shown in FIG. 9, second comparator tree 720 may includemultiple comparators arranged in a tournament tree fashion to find aparticular minimum M value for states S11 or S12 among a number of Mvalues that affect those respective states.

Third comparator tree 730 receives 16 M values 322 and compares the Mvalues that affect states S1-S8 to find the minimum among them. Also,third comparator tree 730 receives a previously stored minimum value forS1-8 from storage device 760 and compares those values to the newlycomputed minimum values to find the minimum among them. Third comparatortree 730 outputs and stores the minimum state S1-8 values as the newminimum values for those states in storage device 760.

FIG. 10 is an exemplary implementation of third comparator tree 730. Inparticular, as shown in FIG. 9, third comparator tree 730 may includemultiple comparators arranged in a tournament tree fashion to find aparticular minimum M value for states S1-8 among a number of M valuesthat affect those respective states.

At the end of four clock cycles, when all of the M values from each ofthe trees 400 have been computed and compared, the state storage devices740, 750 and 760 store the minimum M values among all of the trees 400that affect the respective state.

In another embodiment, at each clock cycle of the pipeline a differentstream of received signal y is used in computing the metric or M values.FIG. 12 is a detailed illustration of an ML decoding pipeline 1200 inaccordance with this embodiment of the present invention. In particular,ML decoding pipeline 1200 is an alternate implementation for ML decoderpipeline 120 (FIG. 1).

ML decoding pipeline 1200 includes adder circuitry 1210, adder/absolutevalue circuitry 1220 a-d, accumulator/minimum S value circuitry 1230a-d, and LLR circuitry 1240. Adder circuitry 1210 is an implementationof metric state portion computation circuitry 310 (FIG. 1). Addercircuitry 1210 computes y−h₀*x₀, h₁*x₁, and h₂*x₂ for every stream ofthe received signal, one stream per clock cycle. In particular, duringthe first clock cycle, adder circuitry 1210 computes y₀−h_(0,0)*x₀,h_(0,1)*x₁, and h_(0,2)*x₂; during the second clock cycle, addercircuitry 1210 computes y₁−h_(1,0)*x₀, h_(1,1)*x₁, and h_(1,2)*x₂; andduring the third clock cycle, adder circuitry 1210 computesy₂−h_(2,0)*x₀, h_(2,1)*x₁, and h_(2,2)*x₂. Because each x can be one offour values, each of the computed portions is a different combination ofthe four values.

Adder circuitry 1210 then performs a subtraction of every permutation oftwo of the computed values in each subsequent clock cycle for eachstream. For example, in the second clock cycle, adder circuitry 1210computes y₀−h_(0,0)*x₀−h_(0,1)*x₁ for every permutation of x₀ and x₁since the computation of y₀−h_(0,0)x₀ and h_(0,1)*x₁ is completed in thefirst clock cycle. Thus, at the end of three clock cycles, everypermutation of the bottom two levels of each sub-tree of trees 400 iscomputed. What remains after the three clock cycles, is the combinationof every permutation of the top levels of each sub-tree with thecomputed permutations of the bottom two levels. During each subsequentclock cycle (beginning in the third clock cycle) where different streamsof the input signal are computed, the bottom two levels of one of thesub-trees (e.g., all the permutations of y₀−h_(0,0)*x₀−h_(0,1)*x₁) areprovided to adder/absolute value circuitry 1220 a to compute, forexample, y₀−h_(0,0)*x₀−h_(0,1)*x₁−h_(0,2)*x₂ for one permutation of x₂.This results in computation of one complete sub-tree of trees 400 forone of the three streams in three clock cycles. Adder/absolute valuecircuitry 1220 a also compute the absolute values ofy₀−h_(0,0)*x₀−h_(0,1)*x₁−h_(0,2)*x₂ for the one permutation of x₂ andoutputs |y₀−h_(0,0)x₀−h_(0,1)*x₁−h_(0,2)*x₂| for that permutation of x₂to accumulator/minimum S value circuitry 1230 a.

Each adder/absolute value circuitry 1220 b-d operates in a similarmanner as adder/absolute value circuitry 1220 a but computes|y₀−h_(0,0)*x₀−h_(0,1)*x₁−h_(0,2)*x₂| for different permutations of x₂.Thus, each adder/absolute value circuitry 1220 a-d outputs 16 M valuescorresponding to a particular permutation of x₂ and every permutation ofx₀ and x₁ at each clock cycle. However, each set of 16 M valuescorresponds to a different stream of the input signal y. Therefore, eachadder/absolute value circuitry 1220 a-d takes three clock cycles tocompute all of the 16 M values associated with that particularpermutation of x₂ for every one of the three streams of the receivedsignal y. Adder/absolute value circuitry 1220 a-d thus is oneimplementation of metric logic circuitry 320 (FIG. 3) which provides thevarious M values to state update circuitry 330.

Accumulator/minimum S value circuitry 1230 a-d, at each clock cycle,accumulate the received M values associated with one stream with thoseassociated with another stream of the received signal and compute theminimum values of the accumulated M values that affect a particularstate. For example, as discussed above, state S7 is affected by M₁₋₄ andM₉₋₁₂ and thus, accumulator/minimum S value circuitry 1230 a willcompute the minimum among those M values for state S7. The states thatare affected by the M values can be determined from trees 400 (FIG. 4).Accumulator/minimum S value circuitry 1230 a-d thus are oneimplementation of state update circuitry 330 (FIG. 3).

Each accumulator/minimum S value circuitry 1230 b-d operates in asimilar manner as accumulator/minimum S value circuitry 1230 a toaccumulate and compute the minimum M values that are associated with adifferent permutation of x₂ and that affect a particular state value.Additionally, each accumulator/minimum S value circuitry 1230 b-dcompares the computed minimum S value that it computes with one that aprevious accumulator/minimum S value circuitry 1230 b-d computes. Thisis necessary to find the minimum M values that are associated with eachpermutation of x₂. For example, accumulator/minimum S value circuitry1230 b computes the minimum value among the M values that affect stateS5 that are received from adder/absolute value circuitry 1220 b (e.g.,permutation of x₂=“01”) and compares those values with the minimumcompute value for state S5 received from accumulator/minimum S valuecircuitry 1230 a to find and set state S5 to be the minimum among allthose values.

The last accumulator/minimum S value circuitry 1230 d computes theminimum S values among all of the permutations of x₂ and outputs each ofthose state values (S1-12) to LLR circuitry 1240. At the end of sevenclock cycles, accumulator/minimum S circuitry 1230 d completes computingthe minimum state values for each of states S1-12 for one of the threestreams of received signal y.

LLR circuitry 1240 operates in a similar manner as LLR computationcircuitry 340 and computes the LLR values for each bit position of thereceived signal based on the minimum state values S1-12 that arecomputed by accumulator/minimum S circuitry 1230 d.

FIG. 13 illustrates a process 1300 performed by ML decoding pipeline inaccordance with an embodiment of the present invention. At step 1310 aplurality of metric state portions that correspond to a first tone ofthe input signal are computed in N clock cycles, where a metric state isa difference between the received input signal values and expectedvalues. For example, as shown in connection with FIGS. 3 and 5, metricstate portion computation circuitry 310 and 500, receive and compute, infour clock cycles, for at least one of the streams of one tone ofreceived signal y and channel response H, u=y−H₁*x₁−H₂*x₂−H₃*x₃ forevery permutation of x₂, x₃ and one permutation of x₁. In particular,the lower two levels of at least one of trees 400 are computed by metricstate portion computation circuitry 310. Also as shown in connectionwith FIG. 12, adder circuitry 1210 receives and computes for at leastone of the streams of one tone of received signal y and channel responseH, y−H₁*x₁−H₂*x₂ and H₃*x₃. A first part of adder/absolute valuecircuitry 1220 a-d computes u=y−H₁*x₁−H₂*x₂−H₃*x₃ for every permutationof x₀, x₁ and one permutation of x₂ thus producing the lower two levelsof at least one of trees 400 corresponding to a first one of thereceived streams every clock cycle.

At step 1320 during the next N clock cycles, a first plurality of metricstates are computed based on the metric state portions and the first bitpermutation of the first of the multiple stream input signals. Forexample, metric logic circuitry 320 and 600, receive and compute, in twoclock cycles or stages, for at least one of the streams of one tone ofreceived signal y, |u| for every permutation of x₂, x₃ and onepermutation of x₁ and accumulate the |u| associated with each stream ofreceived signal y which produce 16 metric state values (M) correspondingto that permutation of x₁ (FIGS. 3 and 6). Also as shown in connectionwith FIG. 12, adder/absolute value circuitry 1220 a-d compute for atleast one of the streams of one tone of received signal y, 16 metricstate values (M) corresponding to one permutation of x₂ (FIGS. 3 and 6).

At step 1340, a plurality of minimum state update values are computedfor each bit position of the input signal by comparing the firstplurality of metric states with a second plurality of metric states,where the second plurality of metric states correspond to second bitpermutation of the first stream different from the first bitpermutation. For example, state update circuitry 330 and 700, receivethe metric values (M) associated with one permutation of x₁ and comparethe M values affecting a particular state to find the minimum among them(FIGS. 3 and 7-10). State update circuitry 330 and 700 then compare thecomputed minimum values of M with previously computed minimum values(which are associated with a different permutation of xJ of the affectedstate to find the minimum among them. Similarly, accumulator/minimum Svalue circuitry 1230 a-d each compute the minimum state values from thereceived M values for a particular stream of the received signal andcompare that minimum value with one computed by a previousaccumulator/minimum S value circuitry 1230 a-d (which is associated witha different permutation of x₂) to find the minimum value for that state(FIG. 12).

At step 1350, for each bit position of each stream, state update valuesthat correspond to different values of a particular bit position arecombined to provide a plurality of LLR values for each respective bitposition. For example, as shown in connection with FIGS. 3 and 12, LLRcomputation circuitry 340 and 1240, receive the minimum state values forstates S1-12 and compute the LLR for each bit position of the receivedsignal streams. This is done by taking the difference between two statevalues that are associated with a particular bit position of aparticular stream as shown in connection with FIG. 2.

Referring now to FIGS. 14A-14G, various exemplary implementations of thepresent invention are shown.

Referring now to FIG. 14A, the present invention can be implemented in ahard disk drive (HDD) 1400. The present invention may implement eitheror both signal processing and/or control circuits, which are generallyidentified in FIG. 14A at 1402. In some implementations, the signalprocessing and/or control circuit 1402 and/or other circuits (not shown)in the HDD 1400 may process data, perform coding and/or encryption,perform calculations, and/or format data that is output to and/orreceived from a magnetic storage medium 1406.

The HDD 1400 may communicate with a host device (not shown) such as acomputer, mobile computing devices such as personal digital assistants,cellular phones, media or MP3 players and the like, and/or other devicesvia one or more wired or wireless communication links 1408. The HDD 1400may be connected to memory 1409 such as random access memory (RAM), lowlatency nonvolatile memory such as flash memory, read only memory (ROM)and/or other suitable electronic data storage.

Referring now to FIG. 14B, the present invention can be implemented in adigital versatile disc (DVD) drive 1410. The present invention mayimplement either or both signal processing and/or control circuits,which are generally identified in FIG. 14B at 1412, and/or mass datastorage 1418 of the DVD drive 1410. The signal processing and/or controlcircuit 1412 and/or other circuits (not shown) in the DVD drive 1410 mayprocess data, perform coding and/or encryption, perform calculations,and/or format data that is read from and/or data written to an opticalstorage medium 1416. In some implementations, the signal processingand/or control circuit 1412 and/or other circuits (not shown) in the DVDdrive 1410 can also perform other functions such as encoding and/ordecoding and/or any other signal processing functions associated with aDVD drive.

The DVD drive 1410 may communicate with an output device (not shown)such as a computer, television or other device via one or more wired orwireless communication links 1417. The DVD drive 1410 may communicatewith mass data storage 1418 that stores data in a nonvolatile manner.The mass data storage 1418 may include a hard disk drive (HDD). The HDD1400 may have the configuration shown in FIG. 14A. The HDD 1400 may be amini HDD that includes one or more platters having a diameter that issmaller than approximately 1.8″. The DVD drive 1410 may be connected tomemory 1419 such as RAM, ROM, low latency nonvolatile memory such asflash memory and/or other suitable electronic data storage.

Referring now to FIG. 14C, the present invention can be implemented in ahigh definition television (HDTV) 1420. The present invention mayimplement either or both signal processing and/or control circuits,which are generally identified in FIG. 14C at 1422, a WLAN interfaceand/or mass data storage of the HDTV 1420. The HDTV 1420 receives HDTVinput signals in either a wired or wireless format and generates HDTVoutput signals for a display 1426. In some implementations, signalprocessing circuit and/or control circuit 1422 and/or other circuits(not shown) of the HDTV 1420 may process data, perform coding and/orencryption, perform calculations, format data and/or perform any othertype of HDTV processing that may be required.

The HDTV 1420 may communicate with mass data storage 1427 that storesdata in a nonvolatile manner such as optical and/or magnetic storagedevices for example hard disk drives and/or DVD drives. At least one HDDmay have the configuration shown in FIG. 14A and/or at least one DVDdrive may have the configuration shown in FIG. 14B. The HDD may be amini HDD that includes one or more platters having a diameter that issmaller than approximately 1.8″. The HDTV 1420 may be connected tomemory 1428 such as RAM, ROM, low latency nonvolatile memory such asflash memory and/or other suitable electronic data storage. The HDTV1420 also may support connections with a WLAN via a WLAN interface 1429.

Referring now to FIG. 14D, the present invention implements a controlsystem of a vehicle 1430, a WLAN interface and/or mass data storage ofthe vehicle control system. In some implementations, the presentinvention may implement a powertrain control system 1434 that receivesinputs from one or more sensors such as temperature sensors, pressuresensors, rotational sensors, airflow sensors and/or any other suitablesensors and/or that generates one or more output control signals such asengine operating parameters, transmission operating parameters, brakingparameters, and/or other control signals.

The present invention may also be implemented in other control systems1439 of the vehicle 1430. The control system 1439 may likewise receivesignals from input sensors 1437 and/or output control signals to one ormore output devices 1438. In some implementations, the control system1439 may be part of an anti-lock braking system (ABS), a navigationsystem, a telematics system, a vehicle telematics system, a lanedeparture system, an adaptive cruise control system, a vehicleentertainment system such as a stereo, DVD drive, compact disc drive andthe like. Still other implementations are contemplated.

The powertrain control system 1434 may communicate with mass datastorage 1431 that stores data in a nonvolatile manner. The mass datastorage 1431 may include optical and/or magnetic storage devices forexample hard disk drives and/or DVD drives. At least one HDD may havethe configuration shown in FIG. 14A and/or at least one DVD drive mayhave the configuration shown in FIG. 14B. The HDD may be a mini HDD thatincludes one or more platters having a diameter that is smaller thanapproximately 1.8″. The powertrain control system 1434 may be connectedto memory 1432 such as RAM, ROM, low latency nonvolatile memory such asflash memory and/or other suitable electronic data storage. Thepowertrain control system 1434 also may support connections with a WLANvia a WLAN interface 1433. The control system 1439 may also include massdata storage, memory and/or a WLAN interface (all not shown).

Referring now to FIG. 14E, the present invention can be implemented in acellular phone 1450 that may include a cellular antenna 1451. Thepresent invention may implement either or both signal processing and/orcontrol circuits, which are generally identified in FIG. 14E at 1452, aWLAN interface and/or mass data storage of the cellular phone 1450. Insome implementations, the cellular phone 1450 includes a microphone1456, an audio output 1458 such as a speaker and/or audio output jack, adisplay 1460 and/or an input device 1462 such as a keypad, pointingdevice, voice actuation and/or other input device. The signal processingand/or control circuits 1452 and/or other circuits (not shown) in thecellular phone 1450 may process data, perform coding and/or encryption,perform calculations, format data and/or perform other cellular phonefunctions.

The cellular phone 1450 may communicate with mass data storage 1464 thatstores data in a nonvolatile manner such as optical and/or magneticstorage devices for example hard disk drives and/or DVD drives. At leastone HDD may have the configuration shown in FIG. 14A and/or at least oneDVD drive may have the configuration shown in FIG. 14B. The HDD may be amini HDD that includes one or more platters having a diameter that issmaller than approximately 1.8″. The cellular phone 1450 may beconnected to memory 1466 such as RAM, ROM, low latency nonvolatilememory such as flash memory and/or other suitable electronic datastorage. The cellular phone 1450 also may support connections with aWLAN via a WLAN interface 1468.

Referring now to FIG. 14F, the present invention can be implemented in aset top box 1460. The present invention may implement either or bothsignal processing and/or control circuits, which are generallyidentified in FIG. 14F at 1468, a WLAN interface and/or mass datastorage of the set top box 1460. The set top box 1460 receives signalsfrom a source such as a broadband source and outputs standard and/orhigh definition audio/video signals suitable for a display 1469 such asa television and/or monitor and/or other video and/or audio outputdevices. The signal processing and/or control circuits 1468 and/or othercircuits (not shown) of the set top box 1460 may process data, performcoding and/or encryption, perform calculations, format data and/orperform any other set top box function.

The set top box 1460 may communicate with mass data storage 1462 thatstores data in a nonvolatile manner. The mass data storage 1462 mayinclude optical and/or magnetic storage devices for example hard diskdrives and/or DVD drives. At least one HDD may have the configurationshown in FIG. 14A and/or at least one DVD drive may have theconfiguration shown in FIG. 14B. The HDD may be a mini HDD that includesone or more platters having a diameter that is smaller thanapproximately 1.8″. The set top box 1460 may be connected to memory 1464such as RAM, ROM, low latency nonvolatile memory such as flash memoryand/or other suitable electronic data storage. The set top box 1460 alsomay support connections with a WLAN via a WLAN interface 1466.

Referring now to FIG. 14G, the present invention can be implemented in amedia player 1470. The present invention may implement either or bothsignal processing and/or control circuits, which are generallyidentified in FIG. 14G at 1474, a WLAN interface and/or mass datastorage of the media player 1470. In some implementations, the mediaplayer 1470 includes a display 1476 and/or a user input 1477 such as akeypad, touchpad and the like. In some implementations, the media player1470 may employ a graphical user interface (GUI) that typically employsmenus, drop down menus, icons and/or a point-and-click interface via thedisplay 1476 and/or user input 1477. The media player 1470 furtherincludes an audio output 1475 such as a speaker and/or audio outputjack. The signal processing and/or control circuits 1474 and/or othercircuits (not shown) of the media player 1470 may process data, performcoding and/or encryption, perform calculations, format data and/orperform any other media player function.

The media player 1470 may communicate with mass data storage 1471 thatstores data such as compressed audio and/or video content in anonvolatile manner. In some implementations, the compressed audio filesinclude files that are compliant with MP3 format or other suitablecompressed audio and/or video formats. The mass data storage 1471 mayinclude optical and/or magnetic storage devices for example hard diskdrives and/or DVD drives. At least one HDD may have the configurationshown in FIG. 14A and/or at least one DVD drive may have theconfiguration shown in FIG. 14B. The HDD may be a mini HDD that includesone or more platters having a diameter that is smaller thanapproximately 1.8″. The media player 1470 may be connected to memory1472 such as RAM, ROM, low latency nonvolatile memory such as flashmemory and/or other suitable electronic data storage. The media player1470 also may support connections with a WLAN via a WLAN interface 1473.Still other implementations in addition to those described above arecontemplated.

The foregoing describes systems and methods providing a decodingpipeline for QPSK MIMO OFDM receivers. The above described embodimentsof the present invention are presented for the purposes of illustrationand not of limitation. Furthermore, the present invention is not limitedto a particular implementation. The invention may be implemented inhardware, such as on an application specific integrated circuit (ASIC)or on a field-programmable gate array (FPGA). The invention may also beimplemented in software.

1. A method for performing pipeline computation of a reliability value,the method comprising: computing, with control circuitry in a firstpipeline stage, a reliability value portion based on a differencebetween a received input signal value and an expected value; combining,in a second pipeline stage that follows the first pipeline stage, thereliability value portion with a first value derived from the receivedinput signal to generate a first reliability value; and determiningwhether to update the first reliability value in a third pipeline stagebased on a combination of the first reliability value with a secondreliability value that corresponds to a second value derived from thereceived input signal.
 2. The method of claim 1, wherein the determiningcomprises computing a plurality of minimum state update values bycomparing the first reliability value with the second reliability value,where the second value that is derived from the input signal correspondsto a second bit permutation of a stream of the input signal.
 3. Themethod of claim 1, wherein the reliability value portion is computedbased on all of the bit permutations of some streams of the inputsignal.
 4. The method of claim 1, further comprising computing, for eachbit position of the input signal, a difference between two state updatevalues that correspond to different values of a particular bit position.5. The method of claim 1, wherein the first pipeline stage is performedin N clock cycles and the second pipeline stage is performed during anext N clock cycles.
 6. The method of claim 1, wherein first reliabilityvalue is updated at each clock cycle and wherein a plurality ofreliability values are computed every four clock cycles.
 7. The methodof claim 1, wherein the combining comprises computing a first pluralityof metric states based on the reliability value portion and a first bitpermutation of the input signal.
 8. The method of claim 1, wherein thereliability value portion corresponds to a first tone of the inputsignal.
 9. The method of claim 1, wherein: the first reliability valueis computed in accordance with:|u|=Σ|u _(i) |=Σ|y _(i) −H _(i) *x _(i)| where u is the firstreliability value, y_(i) is the input signal, i is a stream index to astream of the input signal, H is the channel response, and x is anexpected transmitted signal vector.
 10. The method of claim 9, whereinthe reliability value portion is computed in accordance with:y_(i)−H_(i)*z_(i) where z is a subset of some but not all of the vectorsof x.
 11. A system for performing pipeline computation of a reliabilityvalue, the system comprising: control circuitry configured to: compute,in a first pipeline stage, a reliability value portion based on adifference between a received input signal value and an expected value;combine, in a second pipeline stage that follows the first pipelinestage, the reliability value portion with a first value derived from thereceived input signal to generate a first reliability value; anddetermine whether to update the first reliability value in a thirdpipeline stage based on a combination of the first reliability valuewith a second reliability value that corresponds to a second valuederived from the received input signal.
 12. The system of claim 11,wherein the control circuitry is further configured to compute aplurality of minimum state update values by comparing the firstreliability value with the second reliability value, where the secondvalue that is derived from the input signal corresponds to a second bitpermutation of a stream of the input signal.
 13. The system of claim 11,wherein the reliability value portion is computed based on all of thebit permutations of some streams of the input signal.
 14. The system ofclaim 11, wherein the control circuitry is further configured tocompute, for each bit position of the input signal, a difference betweentwo state update values that correspond to different values of aparticular bit position.
 15. The system of claim 11, wherein the firstpipeline stage is performed in N clock cycles and the second pipelinestage is performed during a next N clock cycles.
 16. The system of claim11, wherein the first reliability value is updated at each clock cycleand wherein a plurality of reliability values are computed every fourclock cycles.
 17. The system of claim 11, wherein the control circuitryis further configured to compute a first plurality of metric statesbased on the reliability value portion and a first bit permutation ofthe input signal.
 18. The system of claim 11, wherein the reliabilityvalue portion corresponds to a first tone of the input signal.
 19. Thesystem of claim 11, wherein: the first reliability value is computed inaccordance with:|u|=Σ|u _(i) |=Σ|y _(i) −H _(i) *x _(i)| where u is the firstreliability value, y_(i) is the input signal, i is a stream index to astream of the input signal, H is the channel response, and x is anexpected transmitted signal vector.
 20. The system of claim 19, whereinthe reliability value portion is computed in accordance with:y _(i) −H _(i) *z _(i) where z is a subset of some but not all of thevectors of x.